Modeling sound generation in stenosed coronary arteries.
Acoustic measurements obtained from sensitive microphones placed on the chest are being used in a procedure to noninvasively diagnose coronary artery disease. Utilizing specially developed signal processing techniques, the spectral content of isolated diastolic heart sounds has been estimated and usually shows an increase in high-frequency components in patients with occlusive coronary arteries. In order to establish a theory for the origin of these spectral features, a sound source model has been developed which combines an incremental network model of the left coronary artery tree with a transfer function model describing arterial chamber resonant characteristics. The network model predicts flow in both normal and stenosed coronary arteries. From this flow information, the arterial chamber transfer function model predicts the development of acoustic signals from the chamber resonant characteristics. The transfer function of a segment of coronary artery demonstrates two resonance frequencies. These resonance frequencies depend upon the length and diameter of the chamber segment, as well as upon the distal hydraulic impedance loading the segment. The lower resonance frequency can be excited by the usual flow fluctuations (low frequency) in the coronary artery. In cases of stenosis, the wideband spectral characteristics of the turbulence produced by the stenosis excites both the low and high resonance frequencies. In a small sample of patients, the spectra obtained from isolated diastolic acoustic signals recorded by a chest microphone agree well with those predicted by this theory.
Wang
J Z
JZ
Department of Biomedical Engineering, Rutgers University, Piscataway, NJ 08855.
Tie
B
B
Welkowitz
W
W
Semmlow
J L
JL
Kostis
J B
JB
D003326:Coronary Circulation / Q000502:physiology; D003327:Coronary Disease / Q000175:diagnosis*; D006347:Heart Sounds / Q000502:physiology*; D006801:Humans; D008955:Models, Cardiovascular; D011237:Predictive Value of Tests; D012815:Signal Processing, Computer-Assisted